1. Field of the Invention
This invention relates to an approximate reasoning apparatus, especially one which performs approximate reasoning using a membership function given for every conclusion associated with a factor (i.e., an event). Further, the invention relates to an approximate reasoning apparatus having functions for evaluating expert knowledge, combining or synthesizing knowledge and learning knowledge synthesis, etc. Still further, the invention concerns an interface between an approximate reasoning apparatus and human beings. The invention deals also with an apparatus for feeding back information concerning correctness of approximate reasoning, and automatically revising expert knowledge in a knowledge base based upon the information fed back.
2. Description of the Related Art
Approximate reasoning is a method of revising or altering the results of reasoning depending upon information quantities of factors used in order to derive the results of reasoning is known. (For example, see "AN EXPERT SYSTEM WITH THINKING IN IMAGES", by Zhang Hongmin, Preprints of Second IFSA Congress, Tokyo, Jul. 20-25, 1987, p. 765.)
This approximate reasoning method involves using a membership function given for every conclusion relative to a factor to calculate the information quantity of every factor (i.e., the information identifying capability of a factor), and revising or altering the results of reasoning (namely the possibility that a conclusion will hold) depending upon information quantities of factors used in order to derive the conclusion (wherein the revision or alteration involves taking the product of possibility and information quantity), thereby improving the capability to identify the results of reasoning.
In an approximate reasoning apparatus of this kind, the possibility of every conclusion is calculated and then either all possibilities are displayed to inform the human operator or only one or a plurality of the conclusions having the highest possibilities are displayed, thereby providing an output of information relating to a conclusion possibility. Whether or not a conclusion holds good is judged in the same way (for example, by selecting the maximum value of possibility values) with regard to all conclusions.
As a result of the foregoing, certain problems are encountered with the conventional approximate reasoning apparatus. Specifically, since a conclusion having a high possibility does not always hold good, the conventional apparatus is not truly accurate. Further, in a case where the value of a possibility that actually holds good differs for every conclusion, this fact cannot be detected. Another difficulty is that if the possibility that a conclusion will hold good varies dynamically with the passage of time, the conventional apparatus cannot deal with this.
The conventional approximate reasoning apparatus does not take into consideration the weight or significance possessed by a conclusion per se (an example of such weight being the cost and time required for repairs in case of fault diagnosis). Consequently, when the apparatus draws conclusions having almost the same possibility, it is incapable of knowing which conclusion is the most significant. The apparatus cannot detect whether there is a conclusion that should be given priority even though its possibility is not high. Accordingly, it not possible to perform reasoning conforming to the special characteristics of the system (equipment) to which reasoning is being applied. Therefore, in order to adapt approximate reasoning to the characteristics of the system which is the object of reasoning, it is necessary that the knowledge base of the approximate reasoning apparatus be altered.
Still other difficulties arise in the conventional approximate reasoning apparatus. Specifically, since information concerning correctness of the results of reasoning is not fed back, it cannot be determined whether these results are truly correct, whether the knowledge inputted by experts is erroneous and, if it is erroneous, how to correct it. In addition, revision of the knowledge base must be performed with human intervention.
In order to obtain correct results of reasoning regarding a particular factor, it is required that the knowledge be rational as well as the characteristics of the membership function, which is given for every conclusion based upon this knowledge, regarding the factor.
In a case where the results of reasoning deduced from a membership function based upon a constructed knowledge base are erroneous, particularly a case where the knowledge base is the result of the combined knowledge of a plurality of experts, it is required that the user consider which item of expert knowledge is erroneous, namely which item of expert knowledge is actually unfit, and that the user reconstruct the knowledge base by revising the erroneous knowledge and recombine the correct knowledge. This is necessary in order to obtain correct results of reasoning regarding events, namely in order to enhance the rationality of results derived from approximate reasoning.
However, actually locating erroneous knowledge in a knowledge base is a very difficult and troublesome task to perform on the user side. Another drawback is that combining knowledge to reconstruct the knowledge base is time consuming.